Explore the vast range of titles available.

Computable biomedical knowledge (CBK) is: “the result of an analytic and/or deliberative process about human health, or affecting human health, that is explicit, and therefore can be represented and reasned upon using logic, formal standards, and mathematical approaches.” Representing biomedical knowledge in a machine-interpretable, computable form increases its ability to be discovered, accessed, understood, and deployed. Computable knowledge artifacts can greatly advance the potential for implementation, reproducibility, or extension of the knowledge by users, who may include practitioners, researchers, and learners. Enriching computable knowledge artifacts may help facilitate reuse and translation into practice. Following the examples of 10 Simple Rules papers for scientific code, software, and applications, we present 10 Simple Rules intended to make shared computable knowledge artifacts more useful and reusable. These rules are mainly for researchers and their teams who have decided that sharing their computable knowledge is important, who wish to go beyond simply describing results, algorithms, or models via traditional publication pathways, and who want to both make their research findings more accessible, and to help others use their computable knowledge. These rules are roughly organized into 3 categories: planning, engineering, and documentation. Finally, while many of the following examples are of computable knowledge in biomedical domains, these rules are generalizable to computable knowledge in any research domain.

Due to the increasing amount of available published evidence and the continual need to apply and update evidence in practice, we propose a shift in the way evidence generated by learning health systems can be integrated into more traditional evidence reviews. This paper discusses two main mechanisms to close the evidence-to-practice gap: (1) integrating Learning Health System (LHS) results with existing systematic review evidence and (2) providing this combined evidence in a standardized, computable data format. We believe these efforts will better inform practice, thereby improving individual and population health.

BackgroundPhysician-to-physician variation in electronic health record (EHR) documentation not driven by patients’ clinical status could be harmful.ObjectiveMeasure variation in completion of common clinical documentation domains. Identify perceived causes and effects of variation and strategies to mitigate negative effects.DesignSequential, explanatory, mixed methods using log data from a commercial EHR vendor and semi-structured interviews with outpatient primary care practices.ParticipantsQuantitative: 170,332 encounters led by 809 physicians in 237 practices. Qualitative: 40 interviewees in 10 practices.Main MeasuresInterquartile range (IQR) of the proportion of encounters in which a physician completed documentation, for each documentation category. Multilevel linear regression measured the proportion of variation at the physician level.Key ResultsFive clinical documentation categories had substantial and statistically significant (p < 0.001) variation at the physician level after accounting for state, organization, and practice levels: (1) discussing results (IQR = 50.8%, proportion of variation explained by physician level = 78.1%); (2) assessment and diagnosis (IQR = 60.4%, physician-level variation = 76.0%); (3) problem list (IQR = 73.1%, physician-level variation = 70.1%); (4) review of systems (IQR = 62.3%, physician-level variation = 67.7%); and (5) social history (IQR = 53.3%, physician-level variation = 62.2%). Drivers of variation from interviews included user preferences and EHR designs with multiple places to record similar information. Variation was perceived to create documentation inefficiencies and risk patient harm due to missed or misinterpreted information. Mitigation strategies included targeted user training during EHR implementation and practice meetings focused on documentation standardization.ConclusionsPhysician-to-physician variation in EHR documentation impedes effective and safe use of EHRs, but there are potential strategies to mitigate negative consequences.

This project describes the creation of a single searchable resource during the pandemic, called the COVID-19 Best Evidence Front Door, with a primary goal of providing direct access to high-quality meta-analyses, literature syntheses, and clinical guidelines from a variety of trusted sources. The Front Door makes relevant evidence findable and accessible with a single search to aggregated evidence-based resources, optimizing time, discovery, and improved access to quality scientific evidence while reducing the burden of frontline health care providers and other knowledge-seekers in needing to separately identify, locate, and explore multiple websites.

Health care professionals must learn continuously as a core part of their work. As the rate of knowledge production in biomedicine increases, better support for health care professionals' continuous learning is needed. In health systems, feedback is pervasive and is widely considered to be essential for learning that drives improvement. Clinical quality dashboards are one widely deployed approach to delivering feedback, but engagement with these systems is commonly low, reflecting a limited understanding of how to improve the effectiveness of feedback about health care. When coaches and facilitators deliver feedback for improving performance, they aim to be responsive to the recipient's motivations, information needs, and preferences. However, such functionality is largely missing from dashboards and feedback reports. Precision feedback is the delivery of high-value, motivating performance information that is prioritized based on its motivational potential for a specific recipient, including their needs and preferences. Anesthesia care offers a clinical domain with high-quality performance data and an abundance of evidence-based quality metrics. The objective of this study is to explore anesthesia provider preferences for precision feedback. We developed a test set of precision feedback messages with balanced characteristics across 4 performance scenarios. We created an experimental design to expose participants to contrasting message versions. We recruited anesthesia providers and elicited their preferences through analysis of the content of preferred messages. Participants additionally rated their perceived benefit of preferred messages to clinical practice on a 5-point Likert scale. We elicited preferences and feedback message benefit ratings from 35 participants. Preferences were diverse across participants but largely consistent within participants. Participants' preferences were consistent for message temporality (α=.85) and display format (α=.80). Ratings of participants' perceived benefit to clinical practice of preferred messages were high (mean rating 4.27, SD 0.77). Health care professionals exhibited diverse yet internally consistent preferences for precision feedback across a set of performance scenarios, while also giving messages high ratings of perceived benefit. A \"one-size-fits-most approach\" to performance feedback delivery would not appear to satisfy these preferences. Precision feedback systems may hold potential to improve support for health care professionals' continuous learning by accommodating feedback preferences.

Intranasal medications have been proposed as adjuncts to out‐of‐hospital cardiac arrest (OHCA) care. We sought to quantify the effects of intranasal medication administration (INMA) in OHCA workflows. We conducted separate randomized OHCA simulation trials with lay rescuers (LRs) and first responders (FRs). Participants were randomized to groups performing hands‐only cardiopulmonary resuscitation (CPR)/automated external defibrillator with or without INMA during the second analysis phase. Time to compression following the second shock (CPR2) was the primary outcome and compression quality (chest compression rate (CCR) and fraction (CCF)) was the secondary outcome. We fit linear regression models adjusted for CPR training in the LR group and service years in the FR group. Among LRs, INMA was associated with a significant increase in CPR2 (mean diff. 44.1 s, 95% CI: 14.9, 73.3), which persisted after adjustment (p = 0.005). We observed a significant decrease in CCR (INMA 95.1 compressions per min (cpm) vs control 104.2 cpm, mean diff. −9.1 cpm, 95% CI −16.6, −1.6) and CCF (INMA 62.4% vs control 69.8%, mean diff. −7.5%, 95% CI −12.0, −2.9). Among FRs, we found no significant CPR2 delays (mean diff. −2.1 s, 95% CI −15.9, 11.7), which persisted after adjustment (p = 0.704), or difference in quality (CCR INMA 115.5 cpm vs control 120.8 cpm, mean diff. −5.3 cpm, 95% CI −12.6, 2.0; CCF INMA 79.6% vs control 81.2% mean diff. −1.6%, 95% CI −7.4, 4.3%) INMA in LR resuscitation was associated with diminished resuscitation performance. INMA by FR did not impede key times or quality.

Background:The integration of artificial intelligence (AI) into clinical practice is transforming both clinical practice and medical education. AI-based systems aim to improve the efficacy of clinical tasks, enhancing diagnostic accuracy and tailoring treatment delivery. As it becomes increasingly prevalent in health care for high-quality patient care, it is critical for health care providers to use the systems responsibly to mitigate bias, ensure effective outcomes, and provide safe clinical practices. In this study, the clinical task is the identification of heart failure (HF) prior to surgery with the intention of enhancing clinical decision-making skills. HF is a common and severe disease, but detection remains challenging due to its subtle manifestation, often concurrent with other medical conditions, and the absence of a simple and effective diagnostic test. While advanced HF algorithms have been developed, the use of these AI-based systems to enhance clinical decision-making in medical education remains understudied.Objective:This research protocol is to demonstrate our study design, systematic procedures for selecting surgical cases from electronic health records, and interventions. The primary objective of this study is to measure the effectiveness of interventions aimed at improving HF recognition before surgery, the second objective is to evaluate the impact of inaccurate AI recommendations, and the third objective is to explore the relationship between the inclination to accept AI recommendations and their accuracy.Methods:Our study used a 3 × 2 factorial design (intervention type × order of prepost sets) for this randomized trial with medical students. The student participants are asked to complete a 30-minute e-learning module that includes key information about the intervention and a 5-question quiz, and a 60-minute review of 20 surgical cases to determine the presence of HF. To mitigate selection bias in the pre- and posttests, we adopted a feature-based systematic sampling procedure. From a pool of 703 expert-reviewed surgical cases, 20 were selected based on features such as case complexity, model performance, and positive and negative labels. This study comprises three interventions: (1) a direct AI-based recommendation with a predicted HF score, (2) an indirect AI-based recommendation gauged through the area under the curve metric, and (3) an HF guideline-based intervention.Results:As of July 2023, 62 of the enrolled medical students have fulfilled this study’s participation, including the completion of a short quiz and the review of 20 surgical cases. The subject enrollment commenced in August 2022 and will end in December 2023, with the goal of recruiting 75 medical students in years 3 and 4 with clinical experience.Conclusions:We demonstrated a study protocol for the randomized trial, measuring the effectiveness of interventions using AI and HF guidelines among medical students to enhance HF recognition in preoperative care with electronic health record data.International Registered Report Identifier (IRRID):DERR1-10.2196/49842

The concept of a learning health system (LHS) was established nearly 20 years ago as a unifying commitment to speed the generation and use of evidence primarily by leveraging rapid advances in data and technologies, resulting in optimized care for each patient. In the ensuing decades, vanguard adopters of the LHS who have sought to move the LHS from conceptual to operational have done so in ways that fit with and reflect their organizational structure, mission, and culture—as well as their personal values and experiences. They have also extended the focus from health care to include individual and population health more broadly. This commentary describes four distinctive models that have evolved as learning health system activities have matured. Viewing this diversity as a strength, the features, commonalities, and unique differences of these models are described.